347 lines
13 KiB
Python
347 lines
13 KiB
Python
from typing import Any
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import pytest
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from app.core.contracts import AgentRequest, RequestContext
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from app.service.agent.advisor_agent import AdvisorAgent
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from app.service.agent.bootstrap import get_agent_factory
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def request(message: str) -> AgentRequest:
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return AgentRequest(
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agent_type="advisor",
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message=message,
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session_id="advisor-test",
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idempotency_key="advisor-test-request-0001",
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)
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def context(*permissions: str) -> RequestContext:
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return RequestContext(
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user_id="1",
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trace_id="advisor-test",
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roles=("advisor",),
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permissions=("agent:run", *permissions),
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data_scope="all",
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)
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@pytest.mark.asyncio
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async def test_trade_execution_request_is_refused_without_tool_call(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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async def unexpected_tool(**_kwargs: Any) -> object:
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raise AssertionError("交易请求不得调用工具")
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monkeypatch.setattr(agent, "call_tool", unexpected_tool)
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result = await agent.handle(request("请帮我买入 159511"), context())
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assert "不能代您执行" in result.text
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@pytest.mark.asyncio
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async def test_quote_request_uses_public_quote_tool(monkeypatch: pytest.MonkeyPatch) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return [{
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"fund_code": "159511", "fund_name": "测试基金", "nav": "1.20",
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"daily_change": "0.30", "nav_date": "2026-09-10", "quote_source": "cache",
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}]
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(request("请分析 159511 的行情"), context("fund:quote:read"))
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assert calls[0]["name"] == "query_fund_quote"
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assert calls[0]["intent"] == "fund_quote"
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assert "测试基金" in result.text
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@pytest.mark.asyncio
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async def test_financial_query_requires_permission_before_tool_call(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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async def unexpected_tool(**_kwargs: Any) -> object:
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raise AssertionError("无权限时不得调用金融查询工具")
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monkeypatch.setattr(agent, "call_tool", unexpected_tool)
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result = await agent.handle(request("查询我的持仓"), context())
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assert "没有查询金融明细数据的权限" in result.text
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@pytest.mark.asyncio
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async def test_financial_query_uses_public_nl2sql_tool(monkeypatch: pytest.MonkeyPatch) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {
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"status": "success",
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"message": "查询完成",
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"data": {"total": 1, "rows": [{"id": 1}]},
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}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("查询我的持仓"), context("financial:nl2sql:read")
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)
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assert calls[0]["name"] == "query_financial_data"
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assert "1 条记录" in result.text
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@pytest.mark.asyncio
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async def test_historical_transaction_query_is_not_treated_as_execution(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {"status": "success", "message": "查询完成", "data": {"total": 0, "rows": []}}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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await agent.handle(request("查询我的成交记录"), context("financial:nl2sql:read"))
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assert calls[0]["name"] == "query_financial_data"
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@pytest.mark.asyncio
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async def test_suitability_request_uses_public_suitability_tool(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {"allowed": False, "reason_code": "RISK_LEVEL_MISMATCH"}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(request("客户 C3，产品 R4，是否适当"), context("suitability:read"))
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assert calls[0]["name"] == "check_suitability"
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assert calls[0]["arguments"]["customer_risk_level"] == 3
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assert "未通过" in result.text
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@pytest.mark.asyncio
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async def test_investment_goal_without_read_permission_collects_required_facts(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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async def unexpected_tool(**_kwargs: Any) -> object:
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raise AssertionError("无读取权限时不得查询投资目标")
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monkeypatch.setattr(agent, "call_tool", unexpected_tool)
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result = await agent.handle(request("我想设置投资目标"), context())
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assert "期望年化收益区间" in result.text
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assert "最大回撤" in result.text
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assert "投资期限" in result.text
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@pytest.mark.asyncio
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async def test_investment_goal_uses_read_only_tool(monkeypatch: pytest.MonkeyPatch) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {
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"annualized_return_lower_pct": "4.0",
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"annualized_return_upper_pct": "6.0",
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"max_drawdown_pct": "8.0",
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"investment_horizon_months": 36,
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}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("查看我的投资目标书"), context("investment-goal:read:self")
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)
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assert calls[0]["name"] == "query_investment_goal"
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assert calls[0]["intent"] == "investment_goal"
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assert "4.0%-6.0%" in result.text
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@pytest.mark.asyncio
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async def test_asset_allocation_routes_to_read_only_allocation_tool(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {
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"status": "ready",
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"allocation": [
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{"label": "现金管理类场内基金", "target_pct": 30},
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{"label": "债券类场内基金", "target_pct": 50},
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{"label": "权益类场内基金", "target_pct": 20},
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],
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}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("我的资产怎么分配"),
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context(
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"asset-allocation:generate:self",
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"customer-profile:read:self",
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"investment-goal:read:self",
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),
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)
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assert calls[0]["name"] == "generate_asset_allocation"
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assert calls[0]["intent"] == "asset_allocation"
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assert "权益类场内基金：20%" in result.text
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assert "C2" not in result.text
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def test_advisor_declares_flowchart_intents_with_descriptions() -> None:
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expected = {"product_recommend", "portfolio_analysis", "asset_allocation", "comparison"}
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assert expected.issubset(AdvisorAgent.definition.supported_intents)
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assert expected.issubset(AdvisorAgent.definition.intent_descriptions)
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@pytest.mark.asyncio
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async def test_advisor_review_orchestrates_existing_governed_read_only_tools(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[str] = []
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async def call_tool(name: str, _arguments: dict[str, Any], **_kwargs: Any) -> object:
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calls.append(name)
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results: dict[str, object] = {
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"generate_asset_allocation": {
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"status": "ready",
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"allocation": [{"label": "债券类场内基金", "target_pct": 60}],
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"optimization": {"dynamic": True, "metric_coverage_pct": "100.00"},
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},
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"analyze_portfolio": {
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"status": "ready",
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"summary": {"total_market_value": "1000", "product_hhi": "3000"},
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"warnings": [{"message": "集中度需要关注"}],
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},
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"simulate_portfolio_rebalance": {
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"status": "ready",
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"allocation_gaps": [{
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"asset_class": "bond_etf", "current_pct": "30", "target_pct": "60",
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"delta_pct": "30",
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}],
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},
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"recommend_products": {
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"status": "ready",
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"products": [{
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"product_name": "测试ETF", "product_code": "159511", "reason": "适当性匹配",
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}],
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},
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}
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return results[name]
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("请给我完整投顾建议"),
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context(
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"asset-allocation:generate:self", "customer-profile:read:self",
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"investment-goal:read:self", "portfolio-analysis:read:self",
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"product-recommendation:read:self", "suitability:read",
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),
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)
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assert calls == [
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"generate_asset_allocation", "analyze_portfolio", "simulate_portfolio_rebalance",
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"recommend_products",
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]
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assert "动态优化：已启用" in result.text
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assert "不生成买卖清单" in result.text
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@pytest.mark.asyncio
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async def test_product_recommendation_uses_governed_tool_without_exposing_profile(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {
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"status": "ready",
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"products": [{
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"product_name": "测试 ETF",
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"product_code": "159511",
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"reason": "已通过适当性过滤，与您的已确认投资期限相匹配。",
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}],
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"graph_degraded": True,
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}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("有什么适合我的基金可以推荐"),
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context(
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"product-recommendation:read:self",
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"customer-profile:read:self",
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"investment-goal:read:self",
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"suitability:read",
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),
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)
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assert calls[0]["name"] == "recommend_products"
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assert calls[0]["intent"] == "product_recommend"
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assert "测试 ETF" in result.text
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assert "图谱关联信息暂不可用" in result.text
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assert "C3" not in result.text
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@pytest.mark.asyncio
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async def test_portfolio_analysis_uses_read_only_analysis_tool(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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agent = AdvisorAgent(AdvisorAgent.definition)
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calls: list[dict[str, Any]] = []
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async def call_tool(name: str, arguments: dict[str, Any], **kwargs: Any) -> object:
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calls.append({"name": name, "arguments": arguments, **kwargs})
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return {
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"status": "ready",
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"summary": {
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"total_market_value": "1000.00",
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"product_hhi": "5800.00",
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"industry_coverage_pct": "100.00",
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},
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"top_positions": [{
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"product_name": "测试 ETF",
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"product_code": "159511",
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"share_pct": "70.00",
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}],
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"top_industries": [{"industry_name": "科技", "share_pct": "100.00"}],
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"warnings": [{"message": "单一产品持仓占比较高，存在集中度风险。"}],
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"graph_context": {
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"degraded": False,
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"overlaps": [{"industry_name": "科技", "product_count": 2}],
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},
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}
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monkeypatch.setattr(agent, "call_tool", call_tool)
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result = await agent.handle(
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request("分析我的持仓集中度"),
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context("portfolio-analysis:read:self", "customer-profile:read:self"),
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)
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assert calls[0]["name"] == "analyze_portfolio"
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assert calls[0]["intent"] == "portfolio_analysis"
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assert "测试 ETF" in result.text
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assert "集中度风险" in result.text
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assert "图谱关系提示" in result.text
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assert "C3" not in result.text
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def test_advisor_is_registered_in_shared_factory() -> None:
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get_agent_factory.cache_clear()
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try:
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factory = get_agent_factory()
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assert factory.definition("advisor") == AdvisorAgent.definition
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finally:
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get_agent_factory.cache_clear()
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